Performance Analysis of Turning Process via Particle Swarm Optimization
This paper describes the implementation of Particle Swarm Optimization (PSO) technique for CNC (computer numerically controlled) turning problem to find the optimal operating parameters such as cutting speed and feed rate such that the total production ti
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Abstract This paper describes the implementation of Particle Swarm Optimization (PSO) technique for CNC (computer numerically controlled) turning problem to find the optimal operating parameters such as cutting speed and feed rate such that the total production time is minimized subject to the constraints such as cutting force, power, tool-chip interface temperature and surface roughness of the product. An example is given to illustrate the working of Particle Swarm Optimization for optimizing the operating parameters. The results are compared with those obtained by Nelder Mead simplex method (NMS), boundary search method (BSP), genetic algorithm (GA) and simulated annealing (SA).
1 Introduction Optimizing machining conditions has become increasingly important owing to the extensive applications of computer numerical control (CNC) machines. For CNC turning process, determining optimal machining parameters is necessary to satisfy requirements involving machining safety, machining economics and machining quality. Although there are handbooks that provide recommended cutting conditions, they do not consider the economic aspect of machining and also are not suitable for CNC machining. Machining optimization problems have been investigated by many researchers. Gilbert [11] presented a theoretical analysis of optimization of the process by using two criteria, maximum production rate and minimum machining cost. So far these K. Deep Indian Institute of Technology Roorkee, Roorkee India 247667 [email protected] J.C. Bansal Indian Institute of Technology Roorkee, Roorkee India 247667 [email protected] K. Deep and J.C. Bansal: Performance Analysis of Turning Process via Particle Swarm Optimization, Studies in Computational Intelligence (SCI) 129, 453–460 (2008) c Springer-Verlag Berlin Heidelberg 2008 www.springerlink.com
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K. Deep, J.C. Bansal
problems are solved using conventional techniques such as geometric programming [1, 2], convex programming [3], Nelder Mead simplex search method [4, 5], direct search method [9] and non-conventional techniques such as genetic algorithm [7], simulated annealing [8].Details of these techniques can be found in [8]. In this paper, the mathematical model proposed by Agapiou [4,5] is used to determine the optimal operating parameters for CNC turning operations. Total production time is considered as the objective function, subject to constraints such as cutting force, power, tool chip interface temperature and the surface roughness of the product. Particle Swarm Optimization (PSO) technique is used to solve the problem. An example is given to illustrate how PSO is used to determine the optimum operating parameters. Results are compared with those of [8].
2 Model Development The optimization model developed in this research attempts to minimize total time required to machine a part. This time is governed by times necessary for machining, tool changing, tool quick return, and work piece handling. The mathematical formulation of this model is taken from [4, 5]. Tu = tm + tcs (tm
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